BLS at 125: using historic principles to track the 21st-century economy
Bibliographic record
Abstract
The U.S. Bureau of Labor Statistics (BLS) used its centennial in 1984 as “an opportunity to reflect on what we can learn from history and a time to think about emerging problems and their implications” for the future.1 At that time, it would have been hard to imagine the growth and change in the economy over just a quarter century—and the growth and change at the BLS designed to keep up with the changing economy. Remarkably, some things that could not have been imagined in 1984 are now commonplace at the BLS: the use of the Internet for data collection and dissemination, computers on every employee’s desk, staff telecommuting, distance training via video and computer, cognitive review to improve the clarity and accuracy of BLS questionnaires and publications, blogs and wikis, and more. But all of these changes are needed to track an economy that is increasingly global, lightning fast, and constantly being reinvented. Gone are the days when the BLS counted girdle manufacturers and stenographers. To keep up with the world of satellite communications and nanotechnology, the Agency had to reinvent itself. The 100-year anniversary was marked with the publication of a volume that traced the growth of the BLS through the terms of 10 William J. Wiatrowski is Associate Commissioner, Office of Compensation and Working Conditions, Bureau of Labor Statistics. William J. Wiatrowski BLS at 125: using historic principles to track the 21st-century economy
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".